Every delivery business already maintains exact per store zones, because they decide who can order. Most service franchises have contracted territories that do the same job. The best local targeting data a network owns is sitting in the ordering system, unused by the media side. This is the practical path from that polygon to an audience Meta and Google will accept.
A delivery zone is the polygon a store actually serves. An ad audience is whatever a platform will accept as targeting. The job is getting from one to the other without losing the shape. Done once, properly, it replaces every hand-picked radius in the network with targeting derived from the same boundary that decides whether a customer can buy at all.
Why it is worth the effort is covered on radius vs delivery zone. This page is the how.
Export the zone the ordering or dispatch system enforces, not a shape marketing drew for a slide. The operational zone is the one that decides who can buy, which makes it the only defensible boundary to spend against.
Remove holes and stray islands, close gaps, and sanity check the area against what the store believes it serves. In one Sydney cluster the zones run from 5 to 23 square kilometres, and neighbouring zones tile without overlapping. Yours should too.
Platforms do not accept polygons. Fit a set of smaller circles inside the zone so the covered area follows the shape as closely as the platform allows. The union of small circles hugs the polygon; one big circle around the pin does not.
Name the store and the suburbs in the copy and on the image. People inside the zone recognise themselves, and the few outside it self-select away. See brand control for doing that without losing the brand.
Per store budget caps keep spend proportional to the area, and orders rather than reach tell you whether the fit worked. Reach is what the platform sells; orders are what the store keeps. See reporting.
Zones move when stores open, close or re-staff. Refresh the fit from the operational source on a schedule. Targeting that drifts from the real zone is a slow return to the radius problem.
The honest note that belongs in every version of this: the circle fit is an approximation. Some coverage lands just outside the polygon, some edges are thin. Against the alternative, one large radius, the share of spend outside the zone is usually several times smaller: measured across 450 realistic zones, roughly five times smaller, see the zone targeting benchmark. Say that plainly and nobody can use it against you.
For a single store this is an afternoon with a map. For a franchise network it is a system job, and it belongs at head office: the zones already live centrally, the fitting is the same task for every store, and no partner should be solving geometry before they can run a campaign. This is exactly what delivery area targeting automates in Amplaro: head office loads the zones once, and every campaign any partner launches is fitted to their own store's polygon from then on. What that takes off the plate at scale is covered in running LSM at scale.
| Platform | What it accepts | How a zone maps to it |
|---|---|---|
| Meta (Facebook and Instagram) | Points with radii, named localities | A set of smaller circles fitted inside the polygon |
| Google (Search, Maps, YouTube) | Radii and named localities | Fitted circles, or the suburb list where zones follow suburbs |
| Print and letterbox | Distribution rounds and postcodes | Rounds chosen inside the zone, the same boundary again |
Swipe the table sideways to see the rest.
For one store you can. For a network you cannot: hundreds of separately maintained guesses that drift the moment operations redraw a zone. Deriving targeting from the polygon makes it reproducible and updatable in one pass.
Points with a radius, plus named localities. No polygon upload for ordinary campaigns. Hence the fitted circles: their union follows the shape far more closely than one large circle around the pin.
No, and say so. Some coverage falls just outside the polygon, some edges are thin. The comparison that matters is against one big radius, where the outside share is usually several times larger.
Suburb lists translate to platform localities directly. Check what the platform resolved against the list operations maintains, because suburb names collide and boundaries surprise.
Why the circle wastes local budget in the first place, with the arithmetic.
The product page: zones loaded once, every store's campaigns fitted automatically.
What changes when local marketing has to work across hundreds of stores.
If your zones already live in an ordering or territory system, seeing them fitted takes one demo.